Leading procurement organizations today don’t just measure what they spent. They push their definition of spend analysis to encompass their total value contribution to the business, taking advantage of both conventional and newly accessible data sources to enable true supply analytics.

How have they done this? While strong leadership and evolving best practices have played an important role, the simple answer is that analytics technology has finally advanced to the point where it can enable a supply analytics strategy.

Key to this evolution is the rise of artificial intelligence within the enterprise, which is now helping procurement organizations gain new insights and shape new strategies not before possible with standard spend analytics approaches. To understand why, here are three examples of how procurement leaders are taking their analytics strategies to the next level with AI.

Strategic Sourcing

The traditional approach to spend analytics helps procurement organizations reduce, avoid or recover costs with their suppliers. Historical analysis of spend data can help somewhat in the supplier selection process, but processing a typical quarterly batch of data is hardly the most actionable information on which to base a strategic sourcing effort.

The more intelligent route is to expand the role of spend analysis in strategic sourcing. Using a robust data acquisition, cleansing and classification process enabled by the latest machine learning methods, procurement can run spend analysis reports before, during and after a sourcing event — a far more complete picture than an out-of-date snapshot.

Before a sourcing event even begins, the standard spend analysis report can now produce demand breakdowns, common cost component analyses, market analyses and supplier performance analyses, to name just a few. There’s no better preparation than heading into a sourcing event with real-time internal and external data.

Armed with knowledge about suppliers and current market conditions, procurement should then be able to run real-time reports during event, such as breakdowns by product, service and carrier; cross-supplier and cross-carrier comparisons; and variance and outlier analyses. These ensure a data-driven strategy carries over into the award decision, and that procurement is getting the absolute best deal it can from its sourcing process.

Finally, procurement should take advantage of real-time classification technology to analyze spend patterns with the selected supplier post-event. This can include spend-to-date reports, invoice analysis, realized savings analysis and maverick spend analysis, to help keep purchases compliant and tackle pesky tail spend.

Supply Management

Getting a great deal with a supplier is a big win for any procurement team. But after a sourcing event, that supplier becomes just one of many, all of which need to be monitored and evaluated to ensure the relationship is bringing value into the enterprise.

The foundational spend analysis program focuses on determining how much the business spent, with whom, in what quantity, where items were shipped and how they were paid for. The evolutionary approach, however, extends analytics into scenarios beyond the confines of traditional spend analysis such as operations and logistics.

This requires an analytics engine that can ingest semi-structured data and other content beyond just transactional data. Machine learning-enhanced analytics services are the only offerings available that can do this quickly and, most important, at human-level accuracy.

For example, instead of just looking at purchase orders and invoices, leading procurement organizations can run reports on inventory turnover and warehouse utilization, helping them determine inventory overhead costs and predict stockouts. They can also analyze a supply base by geography — new geographies and languages often confound prior spend analysis solutions — as well as provide insights into average fulfillment times, underlying commodity and fuel costs, and other overhead costs.

While these are just a few examples, the savvy procurement professional understands that spend analysis done right, aided by new capabilities unlocked through artificial intelligence, helps businesses understand the total cost of doing business with a supplier. What’s more, this deeper intelligence can be used to compare supplier performance across various benchmarks, presenting procurement with an opportunity to continuously improve the supply services it provides stakeholders.

Risk Management

Beyond value measured in dollars, however, many progressive procurement organizations are expanding their analytics efforts into a critical adjacent territory: risk.

The fundamental value of many AI-based analytics offerings is that they offer cleansed and accurately classified data not before available to procurement organizations without significant time costs. But what makes AI even more of a game-changer is when procurement enriches this data with external content.

In leading analytics platforms, the data sources that can be added on top of spend data go far beyond integrated market price and commodity data feeds. Users can also integrate financial risk scores, sustainability and corporate social responsibility (CSR) scores and similar third-party data sources related to risk.

With this information, procurement can enrich a spend analysis process to see not just how much it spent with a supplier but also whether that spend is in jeopardy because the supplier is teetering toward bankruptcy, or could balloon because the supplier is based in a politically unstable geography, or is tied to an environmentally harmful production process.

Machine learning can uncover trends within these and other key data relationships, leading to broader risk reduction and, in some cases, predictive analytics related to price and margin with suppliers.

Moving Beyond the Foundation

So with all of these potential benefits, why haven’t the majority of procurement organizations taken the initiative to evolve their spend analysis efforts beyond merely analyzing what was spent?

In many ways, the historical flaws of spend analysis providers have made realizing such programs unattainable. But with advances in machine learning and artificial intelligence, particularly the power of deep learning, the gaps in analytics offerings are beginning to narrow. Accurate, real-time classification has become the new standard, and prescriptive intelligence based on community-based benchmarks is brining procurement new insights not possible with previous offerings.

To learn more, stay tuned for the next two installments in this series.